Vetted Python Professionals

Rubeena Riyas

Screened References

Senior AI Engineer specializing in LLM agents, RAG, and scalable data platforms

7y exp

“ML/data engineer who owned an end-to-end production sales analytics pipeline at 15,000+ user scale, delivering ~50% compute reduction, ~80% faster reporting, and ~$1.2M impact. Also shipped a production RAG-based AI assistant over internal BigQuery/docs with evaluation metrics and safety guardrails, and built shared Python libraries to standardize reliability and accelerate engineering teams.”

“UE5/VR-focused developer who has shipped real UE5 deliverables (Fab.com products and VR template projects used by graduate students) and built a modular VR training interaction system end-to-end using Blueprint Interfaces and Actor Components. Also shipped a game-ready UE5 character product (“Ryan the incredible”) with 180 animations and a scalable AnimBP, and has hands-on profiling/optimization experience reducing tick-heavy Blueprint logic.”

“At PwC, built and productionized an agentic RAG enterprise search assistant over 6M internal documents (8M embeddings), deployed across AWS and GCP. Drove major retrieval gains (72%→92% precision via BM25+dense hybrid with RRF and cross-encoder re-ranking), reduced hallucinations 30%, achieved <2s latency at 50–60K queries/month, and cut support tickets 30%—boosting adoption to 2,500 users by adding source-cited answers.”

“Full-stack engineer (T-Mobile experience) focused on authentication/session-management systems, with hands-on work optimizing token-validation flows and reducing latency by eliminating redundant API calls and adding caching. Brings strong production ownership with observability (Splunk/Grafana), Postgres data modeling/index tuning, and resilient async workflow design (idempotency, retries/backoff, queues).”

Khaliun Gerel

Screened ReferencesStrong rec.

Senior Full-Stack Engineer specializing in cloud, web, and mobile platforms

New York, NY7y exp

“Full-stack product engineer who has owned end-to-end delivery of multi-client platforms: Finy (agriculture platform with 3 role-based web dashboards plus 2 field mobile apps) and Ugoku (Japanese studio platform with React/TypeScript dashboards, Node/Mongo backend, and mobile AR video playback). Strong in scalable architecture and performance—offline-first mobile for low connectivity, and AWS-based asynchronous video/AR processing with S3/CloudFront—plus building internal ops tools adopted quickly due to measurable workflow improvements.”

Bradley Govender

Screened ReferencesStrong rec.

Executive data and AI leader specializing in enterprise analytics transformation

Land O' Lakes, FL11y exp

“Product/data/analytics transformation leader with 6+ years driving large-scale modernization across media, healthcare, financial services, and consumer digital products. Notably led a challenging customer analytics transformation at Paramount Streaming and has hands-on experience prototyping LLM-powered analytics workflows while emphasizing governance, semantic layers, and human-in-the-loop deployment.”

JYOTHI N

Screened ReferencesStrong rec.

Senior Data Scientist specializing in analytics, experimentation, and BI on AWS

Austin, TX7y exp

“Data/ML practitioner focused on healthcare data quality and record linkage: analyzed 10M+ records, built anomaly detection and NLP-driven entity resolution, and automated AWS ETL/validation pipelines (Glue/Redshift/Lambda), cutting data errors by 40% and generating $500k in annual savings. Has hands-on experience with embeddings (Sentence Transformers/spaCy), FAISS vector search, and fine-tuning for domain-specific matching.”

Vijay Mohan

Screened ReferencesStrong rec.

Executive technology leader specializing in AI, cloud platforms, and enterprise software

Nashua, NH25y exp

“Founder of Braindump (redfly.ai), an AI companion for memory and personal organization, who has already raised $60,000 from Alchemist Accelerator. Brings hands-on experience with customer discovery, technical validation, and VC pitching, and stands out for a first-principles approach to building products with dramatic performance and cost improvements.”

“Backend engineer with hands-on ownership of Python/Flask microservices and recommendation systems across edtech and telecom. Deployed and operated real-time personalization/recommendation platforms on AWS EKS with Jenkins-based CI/CD, GitOps-style declarative configs, and strong observability practices. Has migration experience moving legacy mixed environments to modern containerized Kubernetes and built Kafka pipelines feeding ML services while managing schema evolution.”

“Full-stack engineer with hands-on experience spanning real-time AI products, large-scale payments migration, internal research infrastructure, and open-source ML tooling. Particularly compelling is the mix of low-latency React/Node/TypeScript systems work, zero-downtime migration of 50,000 accounts across 12 regions, and proactive contributions to Kubeflow build and security reliability.”

Sreenaina Koujala

Screened ReferencesStrong rec.

Mid-level Full-Stack & AI Engineer specializing in cloud and intelligent systems

Ashburn, VA8y exp

“Builder with experience across government contracting, engineering automation, and solo AI product development. They architected a serverless AWS pipeline that converted unstructured BIM data into IFC 3D models, built an enterprise internal-data chatbot with auditability and guardrails at Steampunk, and independently launched an AI study platform using Claude. Strong fit for early-stage or ambiguous environments where end-to-end ownership and practical AI systems matter.”

“Backend/data platform engineer with production experience at Walmart and Molina Healthcare, building Python microservices on AWS (EKS + Lambda) for real-time inventory and recommendation systems. Strong in reliability/observability and incident leadership, plus modernizing legacy healthcare workflows and building resilient AWS Glue/PySpark pipelines with schema evolution and data quality controls.”

“Hands-on LLM practitioner who built a production document-processing pipeline in Python, tackling long-document handling and latency with chunking/batching and a user-driven correction feedback loop. Experienced operationalizing AI workflows with Kubernetes (CronJobs, autoscaling, scheduled data cleaning and weekly retraining) and applying structured testing/evaluation (E2E, LLM-as-judge, HITL) while communicating solutions clearly to non-technical clients using visual diagrams.”

“QA automation engineer with American Express experience owning an end-to-end UI regression suite for critical payment/transaction workflows. Rebuilt the suite with Playwright (BDD/TestNG/POM) and integrated it into CI to catch release-blocking issues like UI/backend payment mismatches and session timeout defects, and applies risk-based test strategy including MFA payment flows.”

“AI engineer (2+ years; pursuing an online MS at UIUC) who has shipped an AI-powered voice screening platform end-to-end on GCP with strong production monitoring and measurable hiring-process impact (80% reduction in unqualified pass-through; ~50+ hours saved per role). Also built and deployed an AWS-based context-aware hybrid search system using OpenSearch as a vector store, and has hands-on experience with multi-agent LLM orchestration (ReAct) and structured-output guardrails.”

“Worked on onboarding a Middle East logistics client processing thousands of invoices/month, building a production-ready pipeline that routes known vendor PDFs to deterministic regex parsers via Tax ID matching and falls back to LlamaParse for unknown layouts. Added financial consistency validation plus human-in-the-loop review and logging/metrics to continuously reduce LLM usage and improve template coverage.”

Keith Aumiller

Screened

Director-level Data & AI Leader specializing in enterprise transformation

Philadelphia, PA22y exp

“Built Cigna Health’s risk-based management/monitoring system used as a primary product for pharmaceutical clients, supporting clinical trials and utilized during the COVID-19 pandemic. Currently running a community-based startup (details kept proprietary) and is open to executive roles or joining a strong team with a clear path to success, evaluating opportunities via ROI.”